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Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.

Nirmal Varghese Babu1, E Grace Mary Kanaga1

  • 1Karunya Institute of Technology and Sciences, Coimbatore, India.

SN Computer Science
|November 24, 2021
PubMed
Summary

Sentiment analysis of social media data aids in understanding public feelings. Multi-class classification using deep learning offers superior accuracy for detecting emotions like apprehension or dejection.

Keywords:
Deep learningDepressionEmoticons & EmojisFeature extractionMachine learningMulticlass classificationNatural language processingSentiment analysisSocial network analysis

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Social Media Analytics

Background:

  • Sentiment analysis is crucial for understanding public opinion and emotions in daily life.
  • Social media platforms provide rich datasets (text, emoticons, emojis) for sentiment analysis.
  • Previous studies often used binary or ternary classification, limiting precision.

Purpose of the Study:

  • To review sentiment analysis techniques on social media data.
  • To focus on detecting apprehension or dejection using artificial intelligence.
  • To compare classification methods for improved sentiment identification.

Main Methods:

  • Utilizing social media data including text, emoticons, and emojis.
  • Applying various artificial intelligence techniques for sentiment analysis.
  • Comparing multi-class classification against binary and ternary approaches.

Main Results:

  • Multi-class classification provides more precise sentiment classification.
  • Deep learning algorithms, particularly within multi-class frameworks, demonstrate higher precision.
  • Social media data is effective for sentiment monitoring and analysis.

Conclusions:

  • Multi-class classification with deep learning significantly enhances sentiment analysis precision.
  • Artificial intelligence techniques are vital for accurate emotion detection from social media.
  • Sentiment analysis on social media offers valuable insights into public apprehension and dejection.